The “Many Body Problem” (aka N-Body Problem) is a problem that appears simple enough but in fact highlights the difficulty of present day mathematics. The many body problem is where you have multiple interacting entities. In Physics, a three-body problem does not have a ‘closed-form’ or analytic solution (see: https://en.wikipedia.org/wiki/Three-body_problem). Something as simple as this reflects the limits of our analytic tools. This does not mean it is not solvable, it only means that we have to resort to approximation and numerical techniques perform the calculation. The three-body problem of the sun, the moon and the earth is can be calculated numerically with sufficient precision to allow a man to land on the moon.
In Deep Learning, there is an emerging N-body problem. Many of the more advanced systems are now tackling the problem where multi-agent system. Each agent will likely have goals (i.e. objective function) that may be cooperative or competitive with the global goals. In multi-agent deep learning system or even in modular deep learning systems, researchers need to devise scalable methods for coordinated work.
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「多体问题」（又叫 N 体问题）是看似简单，实际上在当今数学中极难攻克的问题。多体问题是指多个相互作用的实体。在物理学中，任何三体问题都没有一个封闭的形式或解析解（见：https://en.wikipedia.org/wiki/Three-body_problem）。像这样简单的问题反映了我们分析工具的局限性。这并不意味着它是不可解的，它只意味着我们必须诉诸于近似和数值技术来进行计算。可以用足够精确的数值计算分析太阳、月球和地球之间的三体问题以帮助宇航员登陆月球。
在深度学习领域，我们也有一个新兴的 N 体问题。许多更先进的系统现在正在处理多代理系统的问题。每个代理都可能有与全局目标合作或竞争的目标（即目标函数）。在多代理深度学习系统中，甚至在模块化的深度学习系统中，研究人员需要设计可扩展的合作方法。
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